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#include <utility>
#include "pybind11/pybind11.h"
#include "pybind11/cast.h"
#include "pybind11/stl.h"
#include "cudnn_frontend.h"
#include "pygraph.h"
namespace py = pybind11;
using namespace pybind11::literals;
namespace cudnn_frontend::python_bindings {
std::array<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>, 2>
PyGraph::sdpa(std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& k,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& v,
bool const is_inference,
py::object const& attn_scale,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& bias,
bool const use_alibi_mask,
bool const use_padding_mask,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_kv,
bool const use_causal_mask,
bool const use_causal_mask_bottom_right,
py::object const& sliding_window_length,
py::object const& dropout,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& rng_dump,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& paged_attention_k_table,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& paged_attention_v_table,
py::object const& paged_attention_max_seq_len_kv,
cudnn_frontend::DataType_t const& compute_data_type,
std::string const& name) {
auto attributes = cudnn_frontend::graph::SDPA_attributes()
.set_is_inference(is_inference)
.set_bias(bias)
.set_alibi_mask(use_alibi_mask)
.set_padding_mask(use_padding_mask)
.set_seq_len_q(seq_len_q)
.set_seq_len_kv(seq_len_kv)
.set_causal_mask(use_causal_mask)
.set_causal_mask_bottom_right(use_causal_mask_bottom_right)
.set_compute_data_type(compute_data_type)
.set_name(name);
if (paged_attention_k_table) {
attributes.set_paged_attention_k_table(paged_attention_k_table);
}
if (paged_attention_v_table) {
attributes.set_paged_attention_v_table(paged_attention_v_table);
}
if (!paged_attention_max_seq_len_kv.is_none()) {
if (py::isinstance<py::int_>(paged_attention_max_seq_len_kv)) {
attributes.set_paged_attention_max_seq_len_kv(paged_attention_max_seq_len_kv.cast<int>());
} else {
throw std::runtime_error("paged_attention_max_seq_len_kv must be an integer.");
}
}
if (!attn_scale.is_none()) {
if (py::isinstance<py::float_>(attn_scale)) {
auto const attn_scale_value = attn_scale.cast<float>();
attributes.set_attn_scale(attn_scale_value);
} else {
auto const attn_scale_tensor = attn_scale.cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!attn_scale_tensor) {
throw std::runtime_error("attn_scale must be a cudnn_tensor or float.");
}
attributes.set_attn_scale(attn_scale_tensor);
}
}
if (!sliding_window_length.is_none()) {
int const sliding_window_value = sliding_window_length.cast<int>();
attributes.set_sliding_window_length(sliding_window_value);
}
if (!dropout.is_none()) {
py::tuple dropout_tuple = dropout.cast<py::tuple>();
if ((!dropout_tuple) || (dropout_tuple.size() != 3 && dropout_tuple.size() != 2)) {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor, and an offset tensor) or (mask "
"tensor, scale tensor)");
}
if (py::isinstance<py::float_>(dropout_tuple[0])) {
auto const probability = dropout_tuple[0].cast<float>();
auto const seed = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!seed) {
throw std::runtime_error("dropout seed must be a cudnn_tensor.");
}
auto const offset = dropout_tuple[2].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!offset) {
throw std::runtime_error("dropout offset must be a cudnn_tensor.");
}
attributes.set_dropout(probability, seed, offset);
if (rng_dump) {
attributes.set_rng_dump(rng_dump);
}
} else {
auto const mask = dropout_tuple[0].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!mask) {
throw std::runtime_error("dropout mask must be a cudnn_tensor.");
}
auto const scale = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!scale) {
throw std::runtime_error("dropout scale must be a cudnn_tensor.");
}
attributes.set_dropout(mask, scale);
}
}
// Add page table attributes
auto [O, Stats] = graph.sdpa(q, k, v, attributes);
return {O, Stats};
}
std::array<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>, 3>
PyGraph::sdpa_backward(std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& k,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& v,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& o,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& dO,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& stats,
py::object const& attn_scale,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& bias,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& dBias,
bool const use_alibi_mask,
bool const use_padding_mask,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_kv,
py::object const& max_total_seq_len_q,
py::object const& max_total_seq_len_kv,
bool const use_causal_mask,
bool const use_causal_mask_bottom_right,
py::object const& sliding_window_length,
py::object const& dropout,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& rng_dump,
bool const use_deterministic_algorithm,
cudnn_frontend::DataType_t const& compute_data_type,
std::string const& name) {
auto attributes = cudnn_frontend::graph::SDPA_backward_attributes()
.set_bias(bias)
.set_dbias(dBias)
.set_alibi_mask(use_alibi_mask)
.set_padding_mask(use_padding_mask)
.set_seq_len_q(seq_len_q)
.set_seq_len_kv(seq_len_kv)
.set_causal_mask(use_causal_mask)
.set_causal_mask_bottom_right(use_causal_mask_bottom_right)
.set_deterministic_algorithm(use_deterministic_algorithm)
.set_compute_data_type(compute_data_type)
.set_name(name);
py::object cudnn_tensor_type = py::module_::import("cudnn").attr("tensor");
if (!attn_scale.is_none()) {
if (py::isinstance<py::float_>(attn_scale)) {
auto const attn_scale_value = attn_scale.cast<float>();
attributes.set_attn_scale(attn_scale_value);
} else {
auto const attn_scale_tensor = attn_scale.cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!attn_scale_tensor) {
throw std::runtime_error("attn_scale must be a cudnn_tensor or float.");
}
attributes.set_attn_scale(attn_scale_tensor);
}
}
if (!max_total_seq_len_q.is_none()) {
int const max_total_seq_len_q_value = max_total_seq_len_q.cast<int>();
attributes.set_max_total_seq_len_q(max_total_seq_len_q_value);
}
if (!max_total_seq_len_kv.is_none()) {
int const max_total_seq_len_kv_value = max_total_seq_len_kv.cast<int>();
attributes.set_max_total_seq_len_kv(max_total_seq_len_kv_value);
}
if (!sliding_window_length.is_none()) {
int const sliding_window_value = sliding_window_length.cast<int>();
attributes.set_sliding_window_length(sliding_window_value);
}
if (!dropout.is_none()) {
if (!py::isinstance<py::tuple>(dropout)) {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor"
", and an offset tensor) or (mask tensor, scale tensor)");
}
py::tuple dropout_tuple = dropout.cast<py::tuple>();
if (dropout_tuple.size() != 3) {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor"
", and an offset tensor) or (mask tensor, scale tensor)");
}
if (py::isinstance<py::float_>(dropout_tuple[0]) && py::isinstance(dropout_tuple[1], cudnn_tensor_type) &&
py::isinstance(dropout_tuple[2], cudnn_tensor_type)) {
auto const probability = dropout_tuple[0].cast<float>();
auto const seed = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
auto const offset = dropout_tuple[2].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
attributes.set_dropout(probability, seed, offset);
if (rng_dump) {
attributes.set_rng_dump(rng_dump);
}
} else if (py::isinstance(dropout_tuple[0], cudnn_tensor_type) &&
py::isinstance(dropout_tuple[1], cudnn_tensor_type) &&
py::isinstance(dropout_tuple[2], cudnn_tensor_type)) {
auto const mask = dropout_tuple[0].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
auto const scale = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
auto const scale_inv = dropout_tuple[2].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
attributes.set_dropout(mask, scale, scale_inv);
} else {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor"
", and an offset tensor) or (mask tensor, scale tensor)");
}
}
auto [dQ, dK, dV] = graph.sdpa_backward(q, k, v, o, dO, stats, attributes);
return {dQ, dK, dV};
}
std::array<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>, 4>
PyGraph::sdpa_fp8(std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& k,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& v,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_k,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_v,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_s,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_s,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_o,
bool const is_inference,
py::object const& attn_scale,
bool const use_padding_mask,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_kv,
bool const use_causal_mask,
py::object const& dropout,
cudnn_frontend::DataType_t const& compute_data_type,
std::string const& name) {
auto attributes = cudnn_frontend::graph::SDPA_fp8_attributes()
.set_is_inference(is_inference)
.set_padding_mask(use_padding_mask)
.set_seq_len_q(seq_len_q)
.set_seq_len_kv(seq_len_kv)
.set_causal_mask(use_causal_mask)
.set_compute_data_type(compute_data_type)
.set_name(name);
if (!attn_scale.is_none()) {
if (py::isinstance<py::float_>(attn_scale)) {
auto const attn_scale_value = attn_scale.cast<float>();
attributes.set_attn_scale(attn_scale_value);
} else {
auto const attn_scale_tensor = attn_scale.cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!attn_scale_tensor) {
throw std::runtime_error("attn_scale must be a cudnn_tensor or float.");
}
attributes.set_attn_scale(attn_scale_tensor);
}
}
if (!dropout.is_none()) {
py::tuple dropout_tuple = dropout.cast<py::tuple>();
if ((!dropout_tuple) || (dropout_tuple.size() != 3 && dropout_tuple.size() != 2)) {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor, and an offset tensor) or (mask "
"tensor, scale tensor)");
}
if (py::isinstance<py::float_>(dropout_tuple[0])) {
auto const probability = dropout_tuple[0].cast<float>();
auto const seed = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!seed) {
throw std::runtime_error("dropout seed must be a cudnn_tensor.");
}
auto const offset = dropout_tuple[2].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!offset) {
throw std::runtime_error("dropout offset must be a cudnn_tensor.");
}
attributes.set_dropout(probability, seed, offset);
} else {
auto const mask = dropout_tuple[0].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!mask) {
throw std::runtime_error("dropout mask must be a cudnn_tensor.");
}
auto const scale = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!scale) {
throw std::runtime_error("dropout scale must be a cudnn_tensor.");
}
attributes.set_dropout(mask, scale);
}
}
auto [o, stats, amax_s, amax_o] =
graph.sdpa_fp8(q, k, v, descale_q, descale_k, descale_v, descale_s, scale_s, scale_o, attributes);
return {o, stats, amax_s, amax_o};
}
std::array<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>, 7>
PyGraph::sdpa_fp8_backward(std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& k,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& v,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& o,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& dO,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& stats,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_k,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_v,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_o,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_dO,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_s,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& descale_dP,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_s,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_dQ,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_dK,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_dV,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& scale_dP,
py::object const& attn_scale,
bool const use_padding_mask,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_q,
std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>& seq_len_kv,
bool const use_causal_mask,
py::object const& dropout,
cudnn_frontend::DataType_t const& compute_data_type,
std::string const& name) {
auto attributes = cudnn_frontend::graph::SDPA_fp8_backward_attributes()
.set_padding_mask(use_padding_mask)
.set_seq_len_q(seq_len_q)
.set_seq_len_kv(seq_len_kv)
.set_causal_mask(use_causal_mask)
.set_compute_data_type(compute_data_type)
.set_name(name);
if (!attn_scale.is_none()) {
if (py::isinstance<py::float_>(attn_scale)) {
auto const attn_scale_value = attn_scale.cast<float>();
attributes.set_attn_scale(attn_scale_value);
} else {
auto const attn_scale_tensor = attn_scale.cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
if (!attn_scale_tensor) {
throw std::runtime_error("attn_scale must be a cudnn_tensor or float.");
}
attributes.set_attn_scale(attn_scale_tensor);
}
}
py::object cudnn_tensor_type = py::module_::import("cudnn").attr("tensor");
if (!dropout.is_none()) {
if (!py::isinstance<py::tuple>(dropout)) {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor"
", and an offset tensor) or (mask tensor, scale tensor)");
}
py::tuple dropout_tuple = dropout.cast<py::tuple>();
if (dropout_tuple.size() != 3) {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor"
", and an offset tensor) or (mask tensor, scale tensor)");
}
if (py::isinstance<py::float_>(dropout_tuple[0]) && py::isinstance(dropout_tuple[1], cudnn_tensor_type) &&
py::isinstance(dropout_tuple[2], cudnn_tensor_type)) {
auto const probability = dropout_tuple[0].cast<float>();
auto const seed = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
auto const offset = dropout_tuple[2].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
attributes.set_dropout(probability, seed, offset);
} else if (py::isinstance(dropout_tuple[0], cudnn_tensor_type) &&
py::isinstance(dropout_tuple[1], cudnn_tensor_type) &&
py::isinstance(dropout_tuple[2], cudnn_tensor_type)) {
auto const mask = dropout_tuple[0].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
auto const scale = dropout_tuple[1].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
auto const scale_inv = dropout_tuple[2].cast<std::shared_ptr<cudnn_frontend::graph::Tensor_attributes>>();
attributes.set_dropout(mask, scale, scale_inv);
} else {
throw std::runtime_error(
"dropout must be a tuple of (float probability, a seed tensor"
", and an offset tensor) or (mask tensor, scale tensor)");
}
}
auto [dQ, dK, dV, amax_dQ, amax_dK, amax_dV, amax_dP] = graph.sdpa_fp8_backward(q,
k,
v,
o,
dO,
stats,
descale_q,
descale_k,
descale_v,
descale_o,
descale_dO,
descale_s,
descale_dP,
scale_s,
scale_dQ,
scale_dK,
scale_dV,
scale_dP,
attributes);
return {dQ, dK, dV, amax_dQ, amax_dK, amax_dV, amax_dP};
}
void
init_pygraph_sdpa_submodule(py::class_<PyGraph>& m) {
m.def("sdpa",
&PyGraph::sdpa,
py::arg("q"),
py::arg("k"),
py::arg("v"),
py::arg("is_inference"),
py::arg_v("attn_scale", py::none()),
py::arg_v("bias", nullptr),
py::arg_v("use_alibi_mask", false),
py::arg_v("use_padding_mask", false),
py::arg_v("seq_len_q", nullptr),
py::arg_v("seq_len_kv", nullptr),
py::arg_v("use_causal_mask", false),
py::arg_v("use_causal_mask_bottom_right", false),
py::arg_v("sliding_window_length", py::none()),
py::arg_v("dropout", py::none()),
py::arg_v("rng_dump", nullptr),
py::arg_v("paged_attention_k_table", py::none()),
py::arg_v("paged_attention_v_table", py::none()),
py::arg_v("paged_attention_max_seq_len_kv", py::none()),
py::arg_v("compute_data_type", cudnn_frontend::DataType_t::NOT_SET),
py::arg_v("name", ""),
R"pbdoc(
Perform scaled dot product attention.
Args:
q (cudnn_tensor): The query data.
k (cudnn_tensor): The key data. When page_table_k is provided, 'k' is a container of non-contiguous key data.
v (cudnn_tensor): The value data. When page_table_v is provided, 'v' is a container of non-contiguous value data.
is_inference (bool): Whether it is an inference step or training step.
attn_scale (Optional[Union[float, cudnn_tensor]]): The scale factor for attention. Default is None.
bias (Optional[cudnn_tensor]): The bias data for attention. Default is None.
use_alibi_mask (Optional[bool]): Whether to use alibi mask. Default is False.
use_padding_mask (Optional[bool]): Whether to use padding mask. Default is False.
seq_len_q (Optional[cudnn_tensor]): The sequence length of the query.
seq_len_kv (Optional[cudnn_tensor]): The sequence length of the key.
use_causal_mask (Optional[bool]): Whether to use causal mask. Default is False.
use_causal_mask_bottom_right (Optional[bool]): Whether to use bottom right aligned causal mask. Default is False.
sliding_window_length (Optional[int]): The length of sliding window. Default is None.
dropout (Optional[Union[Tuple[(probability: float, seed: cudnn_tensor, offset: cudnn_tensor)], Tuple[mask: cudnn_tensor, scale: cudnn_tensor]]]): Whether to do dropout. Default is None.
rng_dump (Optional[cudnn_tensor]): Debug tensor to dump the Philox RNG dropout mask. Default is None.
paged_attention_k_table (Optional[cudnn_tensor]): The page table to look up offsets into 'k'
paged_attention_v_table (Optional[cudnn_tensor]): The page table to look up offsets into 'v'
paged_attention_max_seq_len_kv (Optional[integer]): The maximum sequence length for k/v caches when paged attention is active.
compute_data_type (Optional[cudnn.data_type]): The data type for computation. Default is NOT_SET.
name (Optional[str]): The name of the operation.
Returns:
o (cudnn_tensor): The output data.
stats (Optional[cudnn_tensor]): The softmax statistics in case the operation is in a training step.
)pbdoc");
m.def("sdpa_backward",
&PyGraph::sdpa_backward,
py::arg("q"),
py::arg("k"),
py::arg("v"),
py::arg("o"),
py::arg("dO"),
py::arg("stats"),
py::arg_v("attn_scale", py::none()),
py::arg_v("bias", nullptr),
py::arg_v("dBias", nullptr),
py::arg_v("use_alibi_mask", false),
py::arg_v("use_padding_mask", false),
py::arg_v("seq_len_q", nullptr),
py::arg_v("seq_len_kv", nullptr),
py::arg_v("max_total_seq_len_q", py::none()),
py::arg_v("max_total_seq_len_kv", py::none()),
py::arg_v("use_causal_mask", false),
py::arg_v("use_causal_mask_bottom_right", false),
py::arg_v("sliding_window_length", py::none()),
py::arg_v("dropout", py::none()),
py::arg_v("rng_dump", nullptr),
py::arg_v("use_deterministic_algorithm", false),
py::arg_v("compute_data_type", cudnn_frontend::DataType_t::NOT_SET),
py::arg_v("name", ""),
R"pbdoc(
Compute the key, query, value gradients of scaled dot product attention.
Args:
q (cudnn_tensor): The query data.
k (cudnn_tensor): The key data.
v (cudnn_tensor): The value data.
o (cudnn_tensor): The output data.
dO (cudnn_tensor): The output loss gradient.
stats (cudnn_tensor): The softmax statistics from the forward pass.
attn_scale (Optional[Union[float, cudnn_tensor]]): The scale factor for attention. Default is None.
bias (Optional[cudnn_tensor]): The bias data for attention. Default is None.
dBias (Optional[cudnn_tensor]): The dBias data for attention. Default is None.
use_alibi_mask (Optional[bool]): Whether to use alibi mask. Default is False.
use_padding_mask (Optional[bool]): Whether to use padding mask. Default is False.
seq_len_q (Optional[cudnn_tensor]): The sequence length of the query.
seq_len_kv (Optional[cudnn_tensor]): The sequence length of the key.
use_causal_mask (Optional[bool]): Whether to use causal mask. Default is False.
use_causal_mask_bottom_right (Optional[bool]): Whether to use bottom right aligned causal mask. Default is False.
sliding_window_length (Optional[int]): The length of sliding window. Default is None.
dropout (Optional[Union[Tuple[(probability: float, seed: cudnn_tensor, offset: cudnn_tensor)], Tuple[mask: cudnn_tensor, scale: cudnn_tensor]]]): Whether to do dropout. Default is None.
rng_dump (Optional[cudnn_tensor]): Debug tensor to dump the Philox RNG dropout mask. Default is None.
use_deterministic_algorithm (Optional[bool]): Whether to always use deterministic algorithm. Default is False.
compute_data_type (Optional[cudnn.data_type]): The data type for computation. Default is NOT_SET.
name (Optional[str]): The name of the operation.
Returns:
dQ (cudnn_tensor): The query gradient data.
dK (cudnn_tensor): The key gradient data.
dV (cudnn_tensor): The value gradient data.
)pbdoc");
m.def("sdpa_fp8",
&PyGraph::sdpa_fp8,
py::arg("q"),
py::arg("k"),
py::arg("v"),
py::arg("descale_q"),
py::arg("descale_k"),
py::arg("descale_v"),
py::arg("descale_s"),
py::arg("scale_s"),
py::arg("scale_o"),
py::arg("is_inference"),
py::arg_v("attn_scale", py::none()),
py::arg("use_padding_mask"),
py::arg_v("seq_len_q", nullptr),
py::arg_v("seq_len_kv", nullptr),
py::arg_v("use_causal_mask", false),
py::arg_v("dropout", py::none()),
py::arg_v("compute_data_type", cudnn_frontend::DataType_t::NOT_SET),
py::arg_v("name", ""),
R"pbdoc(
Perform scaled dot product attention with fp8 datatype inputs and outputs.
Args:
q (cudnn_tensor): The query data.
k (cudnn_tensor): The key data.
v (cudnn_tensor): The value data.
descale_q (cudnn_tensor): Descale factor for query.
descale_k (cudnn_tensor): Descale factor for key.
descale_v (cudnn_tensor): Descale factor for value.
descale_s (cudnn_tensor): Descale factor for S tensor.
scale_s (cudnn_tensor): Scale factor for S tensor.
scale_o (cudnn_tensor): Scale factor for output.
is_inference (bool): Whether it is an inference step or training step.
attn_scale (Optional[Union[float, cudnn_tensor]]): The scale factor for attention. Default is None.
use_padding_mask (bool): Whether it is an inference step or training step.
seq_len_q (Optional[cudnn_tensor]): The sequence length of the query.
seq_len_kv (Optional[cudnn_tensor]): The sequence length of the key.
use_causal_mask (Optional[bool]): Whether to use causal mask. Default is False.
dropout (Optional[Union[Tuple[(probability: float, seed: cudnn_tensor, offset: cudnn_tensor)], Tuple[mask: cudnn_tensor, scale: cudnn_tensor]]]): Whether to do dropout. Default is None.
compute_data_type (Optional[cudnn.data_type]): The data type for computation. Default is NOT_SET.
name (Optional[str]): The name of the operation.
Returns:
o (cudnn_tensor): The output data.
stats (Optional[cudnn_tensor]): The softmax statistics in case the operation is in a training step.
amax_s (cudnn_tensor): The absolute maximum of S tensor.
amax_o (cudnn_tensor): The absolute maximum of output tensor.
)pbdoc");
m.def("sdpa_fp8_backward",
&PyGraph::sdpa_fp8_backward,
py::arg("q"),
py::arg("k"),
py::arg("v"),
py::arg("o"),
py::arg("dO"),
py::arg("stats"),
py::arg("descale_q"),
py::arg("descale_k"),
py::arg("descale_v"),
py::arg("descale_o"),
py::arg("descale_dO"),
py::arg("descale_s"),
py::arg("descale_dP"),
py::arg("scale_s"),
py::arg("scale_dQ"),
py::arg("scale_dK"),
py::arg("scale_dV"),
py::arg("scale_dP"),
py::arg_v("attn_scale", py::none()),
py::arg_v("use_padding_mask", false),
py::arg_v("seq_len_q", nullptr),
py::arg_v("seq_len_kv", nullptr),
py::arg_v("use_causal_mask", false),
py::arg_v("dropout", py::none()),
py::arg_v("compute_data_type", cudnn_frontend::DataType_t::NOT_SET),
py::arg_v("name", ""),
R"pbdoc(
Compute the key, query, value gradients of scaled dot product attention with fp8 datatype inputs and outputs.
Args:
q (cudnn_tensor): The query data.
k (cudnn_tensor): The key data.
v (cudnn_tensor): The value data.
o (cudnn_tensor): The output data.
dO (cudnn_tensor): The output gradient data.
stats (cudnn_tensor): The softmax statistics in case the operation is in a training step.
descale_q (cudnn_tensor): Descale factor for query.
descale_k (cudnn_tensor): Descale factor for key.
descale_v (cudnn_tensor): Descale factor for value.
descale_o (cudnn_tensor): Descale factor for output.
descale_dO (cudnn_tensor): Descale factor for output gradient.
descale_s (cudnn_tensor): Descale factor for S tensor.
descale_dP (cudnn_tensor): Descale factor for P gradient tensor.
scale_s (cudnn_tensor): Scale factor for S tensor.
scale_dQ (cudnn_tensor): Scale factor for query gradient.
scale_dK (cudnn_tensor): Scale factor for key gradient.
scale_dV (cudnn_tensor): Scale factor for value gradient.
scale_dP (cudnn_tensor): Scale factor for dP gradient.
attn_scale (Optional[Union[float, cudnn_tensor]]): The scale factor for attention. Default is None.
use_padding_mask (bool): Whether it is an inference step or training step.
seq_len_q (Optional[cudnn_tensor]): The sequence length of the query.
seq_len_kv (Optional[cudnn_tensor]): The sequence length of the key.
use_causal_mask (Optional[bool]): Whether to use causal mask. Default is False.
dropout (Optional[Union[Tuple[(probability: float, seed: cudnn_tensor, offset: cudnn_tensor)], Tuple[mask: cudnn_tensor, scale: cudnn_tensor]]]): Whether to do dropout. Default is None.
compute_data_type (Optional[cudnn.data_type]): The data type for computation. Default is NOT_SET.
name (Optional[str]): The name of the operation.
Returns:
dQ (cudnn_tensor): The query gradient data.
dK (cudnn_tensor): The key gradient data.
dV (cudnn_tensor): The value gradient data.
amax_dQ (cudnn_tensor): The absolute maximum of query gradient tensor.
amax_dK (cudnn_tensor): The absolute maximum of key gradient tensor.
amax_dV (cudnn_tensor): The absolute maximum of value gradient tensor.
amax_dP (cudnn_tensor): The absolute maximum of dP tensor.
)pbdoc");
m.attr("scaled_dot_product_flash_attention") = m.attr("sdpa");
m.attr("scaled_dot_product_flash_attention_backward") = m.attr("sdpa_backward");
}
} // namespace cudnn_frontend::python_bindings
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